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PRODID:IEEE vTools.Events//EN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
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BEGIN:VEVENT
DTSTAMP:20260204T183430Z
UID:8FA33844-A201-43A7-9F75-D01CAE91716A
DTSTART;TZID=Asia/Kolkata:20240531T133000
DTEND;TZID=Asia/Kolkata:20240531T143000
DESCRIPTION:Over the past two decades\, the significance of MR image recons
 truction has tremendously increased\, enabling reduced scan time\, improve
 d image quality\, and extraction of additional information from the measur
 ed data. During this period\, MRI has witnessed extensive developments in 
 advanced computational algorithms for image reconstruction\, many of which
  have been fueled by signal processing advances in several areas\, includi
 ng multi-channel sampling\, compressed sensing\, dictionary learning\, low
 -rank and structured low-rank methods. Recently\, also neural networks hav
 e been employed for image reconstruction achieving further improvements in
  scan time and image quality. Most importantly\, some of these techniques 
 have found their way in the products of MRI vendors and show significant i
 mpact in clinical practice. These developments\, together with the advance
 ments in computational hardware have opened a new research field of MRI re
 construction as a computational imaging problem. In this talk\, I will dis
 cuss the framework of MRI reconstruction as a computational imaging proble
 m and the advantages it provides in\n\nenhancing the MR performance thereb
 y addressing important clinical needs.\n\nSpeaker(s): Dr. Mariya Doneva\, 
 \n\nVirtual: https://events.vtools.ieee.org/m/422352
LOCATION:Virtual: https://events.vtools.ieee.org/m/422352
ORGANIZER:ieee.sps.sb.iitkgp@gmail.com
SEQUENCE:13
SUMMARY:IEEE SPS SBC Webinar: Improving MRI speed and image quality through
  model-based reconstruction (By Dr. Mariya Doneva)
URL;VALUE=URI:https://events.vtools.ieee.org/m/422352
X-ALT-DESC:Description: <br /><p dir="ltr">Over the past two decades\, the 
 significance of MR image reconstruction has tremendously increased\, enabl
 ing reduced scan time\, improved image quality\, and extraction of additio
 nal information from the measured data. During this period\, MRI has witne
 ssed extensive developments in advanced computational algorithms for image
  reconstruction\, many of which have been fueled by signal processing adva
 nces in several areas\, including multi-channel sampling\, compressed sens
 ing\, dictionary learning\, low-rank and structured low-rank methods. Rece
 ntly\, also neural networks have been employed for image reconstruction ac
 hieving further improvements in scan time and image quality. Most importan
 tly\, some of these techniques have found their way in the products of MRI
  vendors and show significant impact in clinical practice. These developme
 nts\, together with the advancements in computational hardware have opened
  a new research field of MRI reconstruction as a computational imaging pro
 blem. In this talk\, I will discuss the framework of MRI reconstruction as
  a computational imaging problem and the advantages it provides in</p>\n<p
  dir="ltr">enhancing the MR performance thereby addressing important clini
 cal needs.</p>\n<p>&nbsp\;</p>
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